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[WIP]separation parameter #3279

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[WIP]separation parameter #3279

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42 changes: 26 additions & 16 deletions ppcls/engine/engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
from __future__ import print_function

import os
import gc
import shutil
import copy
import platform
Expand Down Expand Up @@ -316,6 +317,8 @@ def train(self):
self.global_step = 0
uniform_output_enabled = self.config['Global'].get(
"uniform_output_enabled", False)
export_during_train = self.config['Global'].get("export_during_train",
False)

if self.config.Global.checkpoints is not None:
metric_info = init_model(self.config.Global, self.model,
Expand Down Expand Up @@ -403,14 +406,17 @@ def train(self):
prefix=prefix,
loss=self.train_loss_func,
save_student_model=True)
if uniform_output_enabled:
if export_during_train:
save_path = os.path.join(self.output_dir, prefix,
"inference")
self.export(save_path, uniform_output_enabled)
self.export(save_path, export_during_train)
gc.collect()
if self.ema:
ema_save_path = os.path.join(
self.output_dir, prefix, "inference_ema")
self.export(ema_save_path, uniform_output_enabled)
self.export(ema_save_path, export_during_train)
gc.collect()
if uniform_output_enabled:
update_train_results(
self.config, prefix, metric_info, ema=self.ema)
save_load.save_model_info(metric_info, self.output_dir,
Expand All @@ -432,14 +438,17 @@ def train(self):
model_name=self.config["Arch"]["name"],
prefix=prefix,
loss=self.train_loss_func)
if uniform_output_enabled:
if export_during_train:
save_path = os.path.join(self.output_dir, prefix,
"inference")
self.export(save_path, uniform_output_enabled)
self.export(save_path, export_during_train)
gc.collect()
if self.ema:
ema_save_path = os.path.join(self.output_dir, prefix,
"inference_ema")
self.export(ema_save_path, uniform_output_enabled)
self.export(ema_save_path, export_during_train)
gc.collect()
if uniform_output_enabled:
update_train_results(
self.config,
prefix,
Expand All @@ -461,15 +470,18 @@ def train(self):
model_name=self.config["Arch"]["name"],
prefix=prefix,
loss=self.train_loss_func)
if uniform_output_enabled:
if export_during_train:
save_path = os.path.join(self.output_dir, prefix, "inference")
self.export(save_path, uniform_output_enabled)
self.export(save_path, export_during_train)
gc.collect()
if self.ema:
ema_save_path = os.path.join(self.output_dir, prefix,
"inference_ema")
self.export(ema_save_path, uniform_output_enabled)
save_load.save_model_info(metric_info, self.output_dir, prefix)
self.export(ema_save_path, export_during_train)
gc.collect()
self.model.train()
if uniform_output_enabled:
save_load.save_model_info(metric_info, self.output_dir, prefix)

if self.vdl_writer is not None:
self.vdl_writer.close()
Expand Down Expand Up @@ -537,11 +549,9 @@ def infer(self):
save_predict_result(save_path, results)
return results

def export(self,
save_path=None,
uniform_output_enabled=False,
def export(self, save_path=None, export_during_train=False,
ema_module=None):
assert self.mode == "export" or uniform_output_enabled
assert self.mode == "export" or export_during_train
if paddle.distributed.get_rank() != 0:
return
use_multilabel = self.config["Global"].get(
Expand All @@ -555,7 +565,7 @@ def export(self,
model = ExportModel(self.config["Arch"], model
if not ema_module else ema_module, use_multilabel)
if self.config["Global"][
"pretrained_model"] is not None and not uniform_output_enabled:
"pretrained_model"] is not None and not export_during_train:
load_dygraph_pretrain(model.base_model,
self.config["Global"]["pretrained_model"])
model.eval()
Expand Down Expand Up @@ -584,7 +594,7 @@ def export(self,
else:
paddle.jit.save(model, save_path)
if self.config["Global"].get("export_for_fd",
False) or uniform_output_enabled:
False) or export_during_train:
dst_path = os.path.join(os.path.dirname(save_path), 'inference.yml')
dump_infer_config(self.config, dst_path)
logger.info(
Expand Down